A Deep Representation Learning Method for Quantitative Immune Defense Function Evaluation and Its Clinical Applications

ABSTRACT The immune defense function protecting the body from invasive pathogens is a key indicator of an individual's health and lacks of methods for quantitative evaluation. This study introduces ImmuDef, a novel algorithm for precisely and quantitatively assessing anti‐infection immune defense function based on RNA‐seq data. ImmuDef selects immune signatures through comparisons of acquired immunodeficiency syndrome (AIDS) or severe sepsis vs. healthy controls (HC) and reduces dimension to construct a latent space via a variational autoencoder (VAE) model (QImmuDef‐VAE), a representation deep learning model. Based on this model, a defense immune score (DImmuScore) was calculated by measuring the distance between a patient and HC within latent space. We validated ImmuDef on 3202 samples across four immune states: immunodeficiency, immunocompromised, immunocompetent, and immunoactive. As a result, DImmuScore achieves high classification accuracy (mean accuracy: 71.75%–76.25%) among samples with various immune states and infections. Furthermore, DImmuScore can serve as a metric for infectious disease severity, where its gradient directly quantifies disease severity. As an application, DImmuScore can be a strong prognostic indicator, effectively stratifying mortality/survival in both sepsis and COVID‐19 patients with no symptomatic difference. This framework was validated across five infectious diseases, establishing the first quantitative standard for cross‐disease immune defense assessment.